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KDD2018 ダイジェスト @ Data Ship Update Lecture #6

Taro Takaguchi
November 07, 2018

KDD2018 ダイジェスト @ Data Ship Update Lecture #6

Taro Takaguchi

November 07, 2018
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  1. KDD 2018 • 2018/08/19 ~ 23 ։࠵ • ࢀՃొ࿥99Χࠃ͔Β3,377ਓ •

    اۀεϙϯαʔ120ສυϧ • ࿦จ౤ߘ1,480݅ • ʢ͍ͣΕ΋աڈ࠷ߴʣ • νϡʔτϦΞϧ29݅ʢ1೔໨ʣ • ϫʔΫγϣοϓ25݅ʢ2೔໨ʣ • ϋϯζΦϯ8݅ • 3~5ηογϣϯಉ࣌ฒߦ • ଞʹ΋Πϕϯτଟ਺ 2
  2. 3

  3. TUTORIAL ON CAUSAL INFERENCE AND COUNTERFACTUAL REASONING • A. Sharma

    & E. KicimanʢMicrosoft Researchʣ • ҼՌਪ࿦ɿ؍࡯σʔλ͔ΒҼՌؔ܎Λਪఆ͢Δ • ߨٛεϥΠυ https://causalinference.gitlab.io/kdd-tutorial/ ग़యɿߨٛεϥΠυ 6
  4. ODD WORKSHOP ON OUTLIER DETECTION DE-CONSTRUCTED http://www.andrew.cmu.edu/user/lakoglu/odd/index.html 1. Ͳͷσʔλ͕֎Ε஋͔ʁ 2.

    ൑ఆ͞Εͨσʔλ͸Ͳ͏͍͏ҙຯͰʮ֎Ε஋ʯ͔ʁ • 'Making sense of unusual suspects - Finding and Characterizing Outliers' • 'The Outlier Description Problem - Complexity Results, Declarative Formulations and Applications ' • 'Outlier Description and Interpretation’ • 'Outlier Detection for Mining Social Misbehavior' ̐݅ͷট଴ߨԋ 7
  5. OUTLIER DETECTION FOR MINING SOCIAL MISBEHAVIOR Neil ShahʢSnap Researchʣ 1.

    ϑΣΠΫΞΧ΢ϯτʹΑΔϑΥϩʔ਺ͷਫ૿͠ 2. ϘοτʹΑΔϥΠϒετϦʔϜࢹௌ਺ͷਫ૿͠ ग़యɿhttp://www.andrew.cmu.edu/user/lakoglu/odd/Shah-outlier-detection- misbehavior-keynote.pdf 8
  6. APPLYING THE DELTA METHOD IN METRIC ANALYTICS: A PRACTICAL GUIDE

    WITH NOVEL IDEAS • A. Deng, U. Knoblich, & J. LuʢMicrosoftʣ • ෳࡶͳࢦඪͷ৴པ۠ؒΛۙࣅܭࢉ͢Δ • ฏۉ஋ͷൺ • ϥϯμϜׂΓ౰ͯ୯Ґͱूܭ୯Ґ͕ҟͳΔͱ͖
 ʢྫɿA/B ςετ֤܈ͷ΢Σϒηογϣϯ௕ͷฏۉʣ • ύʔηϯλΠϧ఺ ग़యɿhttps://dl.acm.org/citation.cfm?id=3219819.3219919 9
  7. CUSTOMIZED REGRESSION MODEL FOR AIRBNB DYNAMIC PRICING • P. Ye,

    et al.ʢAirbnbʣ • Airbnb ͷϗετ޲͚Ձ֨ʢ೔෇ɾ෺݅ʣఏҊϞσϧ ग़యɿhttps://dl.acm.org/citation.cfm?id=3219830 10
  8. DATA ANALYSIS νʔϜͷงғؾ • ത࢜ɾݚڀऀग़਎ͷϝϯόʔൺ཰͕ߴ͍ • ࿦จͳͲͷ৘ใڞ༗͸೔ৗతʹߦΘΕ͍ͯΔ • ࿦จΛಡΉ໨తɿۀ຿Λޮ཰Խ͢Δ
 ɹɹɹɹɹɹɹɹΞ΢τϓοτͷਫ਼౓ΛߴΊΔ


    ɹɹɹɹɹɹɹɹམͱ݀͠ʹ͸·Βͳ͍Α͏ʹ͢Δ • νʔϜͷڧΈɿ
 αΠΤϯε & ΤϯδχΞϦϯάͷ྆ྠ
 ݸʑਓͷಘͨ஌ݟ͕ϥΠϒϥϦ΍಺੡πʔϧʹ
 औΓࠐ·Εͯ࢓૊ΈԽ͞Ε͍ͯ͘ 12